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Improvements in genetics, technology, and agricultural intensification have increased soybean yields; however, adverse climate conditions may prevent these gains from being fully realized in the future. Higher growing season temperatures reduce soybean yields in key production regions including the US Midwest, and better understanding of the developmental and physiological mechanisms that constrain soybean yield under high temperature conditions is needed. This study tested the response of two soybean cultivars to four elevated temperature treatments (+1.7, +2.6, +3.6, and +4.8 °C) in the field over three growing seasons and identified threshold temperatures for response and linear versus non-linear trait responses to temperature. Yield declined non-linearly to temperature, with decreases apparent when canopy temperature exceeded 20.9 °C for the locally adapted cultivar and 22.7°C for a cultivar adapted to more southern locations. While stem node number increased with increasing temperature, leaf area index decreased substantially. Pod production, seed size, and harvest index significantly decreased with increasing temperature. The seasonal average temperature of even the mildest treatment exceeded the threshold temperatures for yield loss, emphasizing the importance of improving temperature tolerance in soybean germplasm with intensifying climate change.

Predicting the consequences of manipulating genotype (G) and agronomic management (M) on agricultural ecosystem performances under future environmental (E) conditions remains a challenge. Crop modelling has the potential to enable society to assess the efficacy of G × M technologies to mitigate and adapt crop production systems to climate change. Despite recent achievements, dedicated research to develop and improve modelling capabilities from gene to global scales is needed to provide guidance on designing G × M adaptation strategies with full consideration of their impacts on both crop productivity and ecosystem sustainability under varying climatic conditions. Opportunities to advance the multiscale crop modelling framework include representing crop genetic traits, interfacing crop models with large-scale models, improving the representation of physiological responses to climate change and management practices, closing data gaps and harnessing multisource data to improve model predictability and enable identification of emergent relationships. A fundamental challenge in multiscale prediction is the balance between process details required to assess the intervention and predictability of the system at the scales feasible to measure the impact. An advanced multiscale crop modelling framework will enable a gene-to-farm design of resilient and sustainable crop production systems under a changing climate at regional-to-global scales.

Sun-induced chlorophyll fluorescence (SIF) measurements have shown unique potential for quantifying plant physiological stress. However, recent investigations found canopy structure and radiation largely control SIF, and physiological relevance of SIF remains yet to be fully understood. This study aims to evaluate whether the SIF-derived physiological signal improves quantification of crop responses to environmental stresses, by analyzing data at three different spatial scales within the U.S. Corn Belt, i.e. experiment plot, field, and regional scales, where ground-based portable, stationary and space-borne hyperspectral sensing systems are used, respectively. We found that, when controlling for variations in incoming radiation and canopy structure, crop SIF signals can be decomposed into non-physiological (i.e. canopy structure and radiation, 60% ∼ 82%) and physiological information (i.e. physiological SIF yield, ΦF, 17% ∼ 31%), which confirms the contribution of physiological variation to SIF. We further evaluated whether ΦF indicated plant responses under high-temperature and high vapor pressure deficit (VPD) stresses. The plot-scale data showed that ΦF responded to the proxy for physiological stress (partial correlation coefficient, r p= 0.40, p< 0.001) while non-physiological signals of SIF did not respond (p> 0.1). The field-scale ΦF data showed water deficit stress from the comparison between irrigated and rainfed fields, and ΦF was positively correlated with canopy-scale stomatal conductance, a reliable indicator of plant physiological condition (correlation coefficient r= 0.60 and 0.56 for an irrigated and rainfed sites, respectively). The regional-scale data showed ΦF was more strongly correlated spatially with air temperature and VPD (r= 0.23 and 0.39) than SIF (r= 0.11 and 0.34) for the U.S. Corn Belt. The lines of evidence suggested that ΦF reflects crop physiological responses to environmental stresses with greater sensitivity to stress factors than SIF, and the stress quantification capability of ΦF is spatially scalable. Utilizing ΦF for physiological investigations will contribute to improve our understanding of vegetation responses to high-temperature and high-VPD stresses.

Traditional gas exchange measurements are cumbersome, which makes it difficult to capture variation in biochemical parameters, namely the maximum rate of carboxylation measured at a reference temperature (Vcmax25 ) and the maximum electron transport at a reference temperature (Jmax25 ), in response to growth temperature over time from days to weeks. Hyperspectral reflectance provides reliable measures of Vcmax25 and Jmax25 ; however, the capability of this method to capture biochemical acclimations of the two parameters to high growth temperature over time has not been demonstrated. In this study, Vcmax25 and Jmax25 were measured over multiple growth stages during two growing seasons for field-grown soybeans using both gas exchange techniques and leaf spectral reflectance under ambient and four elevated canopy temperature treatments (ambient+1.5, +3, +4.5, and +6°C). Spectral vegetation indices and machine learning methods were used to build predictive models for Vcmax25 and Jmax25 , based on the leaf reflectance. Results showed that these models yielded an R2 of 0.57-0.65 and 0.48-0.58 for Vcmax25 and Jmax25 , respectively. Hyperspectral reflectance captured biochemical acclimation of leaf photosynthesis to high temperature in the field, improving spatial and temporal resolution in the ability to assess the impact of future warming on crop productivity.

Irrigation is an important adaptation to reduce crop yield loss due to water stress from both soil water deficit (low soil moisture) and atmospheric aridity (high vapor pressure deficit, VPD). Traditionally, irrigation has primarily focused on soil water deficit. Observational evidence demonstrates that stomatal conductance is co-regulated by soil moisture and VPD from water supply and demand aspects. Here we use a validated hydraulically-driven ecosystem model to reproduce the co-regulation pattern. Specifically, we propose a plant-centric irrigation scheme considering water supply-demand dynamics (SDD), and compare it with soil-moisture-based irrigation scheme (management allowable depletion, MAD) for continuous maize cropping systems in Nebraska, United States. We find that, under current climate conditions, the plant-centric SDD irrigation scheme combining soil moisture and VPD, could significantly reduce irrigation water use (−24.0%) while maintaining crop yields, and increase economic profits (+11.2%) and irrigation water productivity (+25.2%) compared with MAD, thus SDD could significantly improve water sustainability., Irrigation is the most important use of water. A newly developed irrigation management scheme leads to a significant reduction in water use and increase in economic gains while maintaining crop yields, presenting opportunities for real-world impacts under current and future climate conditions.

Sun-induced chlorophyll fluorescence (SiF) is increasingly used as a proxy for vegetation canopy photosynthesis. While ground-based, airborne, and satellite observations have demonstrated a strong linear relationship between SiF and gross primary production (GPP) at seasonal scales, their relationships at high temporal resolution across diurnal to seasonal scales remain unclear. In this study, far-red canopy SiF, GPP, and absorbed photosynthetically active radiation (APAR) were continuously monitored using automated spectral systems and an eddy flux tower over an entire growing season in a rice paddy. At half-hourly resolution, strong linear relationships between SiF and GPP (R2 = 0.76) and APAR and GPP (R2 = 0.76) for the whole growing season were observed. We found that relative humidity, diffuse PAR fraction, and growth stage influenced the relationships between SiF and GPP, and APAR and GPP, and incorporating those factors into multiple regression analysis led to improvements up to R2 = 0.83 and R2 = 0.88, respectively. Relationships between LUEp (=GPP/APAR) and LUEf (=SiF/APAR) were inconsistent at half-hourly and weak at daily resolutions (R2 = 0.24). Both at diurnal and seasonal time scales with half-hourly resolution, we found considerably stronger linear relationships between SiF and APAR than between either SiF and GPP or APAR and GPP. Overall, our results indicate that for subdiurnal temporal resolution, canopy SiF in the rice paddy is above all a very good proxy for APAR at diurnal and seasonal time scales and that therefore SiF-based GPP estimation needs to take into account relevant environmental information to model LUEp. These findings can help develop mechanistic links between canopy SiF and GPP across multiple temporal scales.

Conventional process-based crop simulation models and agro-land surface models require numerous forcing variables and input parameters. The regional application of these crop simulation models is complicated by factors concerning input data requirements and parameter uncertainty. In addition, the empirical remotely sensed regional scale crop yield estimation method does not enable growth process modeling. In this study, we developed a process-based rice yield estimation model by integrating an assimilate allocation module into the satellite remote sensing-derived and biophysical process-based Breathing Earth System Simulator (BESS). Normalized accumulated gross primary productivity ( G P P n o r m - a c c u ) was used as a scaler for growth development, and the relationships between G P P n o r m - a c c u and dry matter partitioning coefficients were determined from the eddy covariance and biometric measurements at the Cheorwon Rice paddy KoFlux site. Over 95% of the variation in the dry matter allocation coefficients of rice grain could be explained by G P P n o r m - a c c u . The dynamics of dry matter distribution among different rice components were simulated, and the annual grain yields were estimated. BESS-Rice simulated GPP and dry matter partitioning dynamics, and rice yields were evaluated against in-situ measurements at three paddy rice sites registered in KoFlux. The results showed that BESS-Rice performed well in terms of rice productivity estimation, with average root mean square error (RMSE) value of 2.2 g C m−2 d−1 (29.5%) and bias of –0.5 g C m−2 d−1 (–7.1%) for daily GPP, and an average RMSE value of 534.8 kg ha−1 (7.7%) and bias of 242.1 kg ha−1 (3.5%) for the annual yield, respectively. BESS-Rice is much simpler than conventional crop models and this helps to reduce the uncertainty related to the forcing variables and input parameters and can result in improved regional yield estimation. The process-based mechanism of BESS-Rice also enables an agronomic diagnosis to be made and the potential impacts of climate change on rice productivity to be investigated.

Ecosystem conductance, which describes ecosystem regulation of water and carbon exchange and links plant functions with the environment, is a critical component in ecosystem and earth system models. However, the behaviors of ecosystem conductance at the ecosystem level and its responses to environmental conditions are still largely unclear. In this study, half-hourly data of 77 eddy-covariance sites from the FLUXNET2015 dataset were used to compare four ecosystem conductance models at the ecosystem level and determine the most consistent vapor pressure deficit (VPD) dependence across plant functional types for varying soil moisture stress levels at the subdaily time scale. We used leaf-level VPD (VPDl), a better indicator of atmospheric dryness at the leaf level, for canopy-level analysis instead of measured atmospheric VPD. Detection of the best-fitted exponent of VPDl indicates that ecosystem conductance responds to VPD between optimality-theory (i.e., VPD−0.5 dependence) and Leuning’s (i.e., VPD−1 dependence) models. Accounting for different soil moisture stress levels only affected minimum ecosystem conductance and did not affect the exponent and factor of VPDl, indicating limited diurnal soil moisture-VPDl interactions. These results indicate limited interaction between xylem and stomata at subdaily time scales and that soil moisture effects can be simplified as a regulation of minimum (soil plus canopy) conductance.

Digital cover photography (DCP) has emerged as an indirect method to measure gap fraction of vegetation canopies. However, as with other photographic methods, determining camera relative exposure value (REV) and threshold for pixel classification, cause substantial uncertainties in gap fraction estimates. Here we propose a new method to improve the measurement of gap fraction under various solar zenith angles (SZAs), sky conditions, and canopy structures. This method computes gap fractions of ambiguous vegetation or sky pixels using an unsaturated raw image from DCP and a reconstructed sky image from the raw image, thus taking full advantage of the potential of raw image processing. This is combined with pre-classification of pixels that are unambiguously canopy and sky to greatly reduce light scattering effects that are likely to be present within the canopy. To test the sensitivity of the new method, we acquired images at one-hour intervals between 20 and 85° of SZAs under closed, half-closed, and open canopies with REV settings from 0 to −5. The new method showed little variation in gap fractions across the diverse SZAs in closed, half-closed, and open canopies. A perforated panel experiment, which was used to test the accuracy of the estimated gap fractions, confirmed that the new method accurately estimated gap fractions across a range of hole size, gap fractions and SZAs. We conclude that the new method opens new opportunities to estimate gap fractions accurately regardless of solar positions from open to closed canopies, and is a significant advance for accurate and precise monitoring of canopy cover and leaf area index (LAI), and for calibrating and evaluating satellite remote sensing LAI products.

The U.S. Corn Belt, the world's biggest production region for corn and soybean combined, is prone to droughts. Currently 92% of the U.S. Corn Belt croplands are rainfed, and thus are sensitive to interannual climate variability and future climate change. Most prior studies identify the lack of soil moisture as the primary cause of agricultural drought impacts, although water-related stresses are also induced by high atmospheric water demands (i.e., vapor pressure deficit, VPD). Here we empirically attributed the variability of canopy-level stomatal conductance (Gs) and gross primary productivity (GPP) to VPD and soil water supply (i.e. volumetric soil water content, SWC), using eddy-covariance data from seven AmeriFlux eddy covariance sites in maize and soybean fields across the U.S. Corn Belt, which are well represented for the current rainfed part of the Corn Belt croplands. We used three independent approaches, including two statistical models (i.e. a multiple-linear regression model and a semi-empirical, non-linear model) and information theory, to quantify the relationship of Gs (or GPP) with VPD and SWC. The attribution result from the two models shows that VPD explains most of Gs variability (91% and 89%, respectively), and mutual information also attributed 91% of GPP variability to VPD. This finding was robust over the gradients of rainfall and temperature, crop types (maize vs. soybean), and management practices (whether irrigated or not). We reconciled our finding with the previously emphasized importance of precipitation and SWC, by conducting a path analysis, which revealed the causal relationships between precipitation, air temperature (Ta), relative humidity (RH), VPD, SWC, and Gs. We find that precipitation impacts on Gs through reduced RH and Ta to VPD (rather than directly through SWC). With increased VPD robustly projected under climate change, we expect increased crop water stress in the future for the U.S. Corn Belt.

Leaf area index (LAI) is a key variable for characterizing crop growth conditions and estimating crop productivity. Despite continuing efforts to develop LAI estimation algorithms, LAI datasets still need improvement at spatial and temporal resolutions to meet the requirements of agricultural applications. Advancements in data fusion technique and the emergence of new satellite data provide opportunities for LAI data at higher resolutions in both space and time. In this study, we derived new LAI estimations by leveraging novel satellite remote sensing datasets, STAIR fusion (MODIS-Landsat fusion) and Planet Labs' CubeSat data (through a reprocessed pipeline) for a typical agricultural landscape in the U.S. Corn Belt. The STAIR fused data and our reprocessed CubeSat data have both fine spatial resolutions (30 m and 3.125 m, respectively) and high frequencies (daily for both). To reliably estimate LAI from these advanced satellite datasets, we used two methods: inversion of a radiative transfer model (RTM), and empirical relationship with vegetation index (VI) calibrated from field measured LAI. Compared to the ground-truth LAI collected at 36 sites across the study region, reliable approximations were achieved by both LAI estimations based on PROSAIL RTM (STAIR: R2 = 0.69 and root mean squared error (RMSE) = 1.12 (m2 m−2), CubeSat: R2 = 0.76 and RMSE = 1.09 (m2 m−2)), and LAI estimations based on Green Wide Dynamic Range Vegetation Index (GrWDRVI) (STAIR: R2 = 0.75, RMSE = 1.10 (m2 m−2), CubeSat: R2 = 0.76, RMSE = 1.08 (m2 m−2), where validation ground-truth is independent from calibration data). Newly estimated high-resolution LAI data were aggregated at 500 m resolution and compared with MODIS and VIIRS LAI products, revealing substantial uncertainties and biases in these two products. We also demonstrated phenology stage estimation at fine spatial resolutions based on our high-frequency LAI data. The proposed LAI estimation methods at both high spatial resolution and temporal frequency can be applied to the entire U.S. Corn Belt and provide significant advancement to crop monitoring and precision agriculture.

Parks account for a large proportion of green spaces in urban regions, but most previous studies have focused on the values of recreational services in urban parks-carbon uptake by plants in urban parks has been studied less extensively. Urban parks typically form complex landscapes in space and time by integrating multiple species with open canopies. Thus, to better understand canopy photosynthesis in urban park, measuring spatial and temporal variations in photosynthetic parameters and canopy structural variables is essential. Here, we report seasonal and spatial variations in two key photosynthetic parameters ( V cmax and J max which are the maximum rates of carboxylation and electron transport, respectively) and leaf area index (LAI) in Seoul Forest Park. During the peak growing season, we found an eightfold difference (20 to 149 μmol m −2 s −1 ) and fourfold difference (38 to 141 μmol m −2 s −1 ) in V cmax and J max , respectively, across 10 species. Over the seasons, two woody species ( Zelkova serrata (Thunb.) Makino and Prunus yedoensis Matsum ), respectively showed three- to fivefold differences in V cmax and two- to fivefold differences in J max . We evaluated whether leaf nitrogen contents could predict V cmax and J max , and found significant correlations among the three variables during the peak growing season across 10 species, but no significant correlations among them over the seasons in the two woody species. LAI computed using in- situ observations and satellite remote-sensing imagery showed a high spatial heterogeneity during the growing season. These results highlight the considerable spatial and temporal variability in photosynthetic parameters and LAI, which implies that individual tree-based three-dimensional canopy modeling will be essential for accurate estimation of canopy photosynthesis in urban parks.

article i nfo Emerging near-surface remote sensing techniques have advanced our ability to monitor forest canopy phenolo- gy. Thus far, however, little effort has been made to monitor the phenologies of the various canopies of multi- layer forestsseparately,despite theirimportance inregulating forestbiogeochemicalcycles. Here we reportphe- nological changesinmulti-layercanopiesofdeciduousbroadleafandevergreenneedleleaf forestsintheRepublic of Korea during the spring of 2013. We installed light-emitting diode (LED) sensors at four different canopy heights at two sites to measure the normalized difference vegetation index (NDVI) using red and near-infrared (NIR) spectral reflectance and to estimate leaf area index (LAI) using the blue band gap fraction. LED-sensors identified leaf-out dates of over- and understory canopies at both sites; leaves unfolded 8-11 days earlier in the understory canopy than the overstory canopy. At the deciduous forest site, LED-NDVI failed to capture the leaf-out date in the overstory canopy, because all four LED-sensors started to see green-up from the understory canopy while the overstory canopy was leafless. LED-LAI identified different leaf-out dates for the over- and un- derstory canopy, because the gap fraction was measured explicitly for each canopy layer. In the evergreen forest site, LED-NDVI signals between the top of the tower and beneath the overstory canopy were decoupled be- cause of the dense evergreen overstory canopy. Both LED-NDVI and LED-LAI identified new needle expan- sion in the overstory canopy and understory canopy development. MODIS NDVI agreed well with LED-NDVI data (R 2 = 0.96, RMSE = 0.04) at the deciduous forest site, and we discovered that understory canopy de- velopment determined the onset of greenness based on MODIS NDVI data. LED-LAI data agreed well with independent estimates from the other instruments, indicating that LED-sensors could be used to monitor multi-layer canopy LAI. Continuous, in-situ observation of multi-layer canopy phenology will aid in the in- terpretation of satellite remote sensing phenology products and improve land surface models that adopt a multi-layer canopy model.